OpenRaiser/Intern-Atlas huggingface dataset:
Fluid Antenna Meets RIS: Random Matrix Analysis and Two-Timescale Design for Multi-User Communications
Venue: arXiv cs.IT 2025
Year: 2025

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) provides significant flexibility in optimizing channel conditions by jointly adjusting the positions of fluid antennas and the phase shifts of RISs. However, it is challenging to acquire the instantaneous channel state information (CSI) for both fluid antennas and RISs, while frequent adjustment of antenna positions and phase shifts will significantly increase the system complexity. To tackle this issue, this paper investigates the two-timescale design for FAS-RIS multi-user systems with linear precoding, where only the linear precoder design requires instantaneous CSI of the end-to-end channel, while the FAS and RIS optimization relies on statistical CSI. The main challenge comes from the complex structure of channel and inverse operations in linear precoding, such as regularized zero-forcing (RZF) and zero-forcing (ZF). Leveraging on random matrix theory (RMT), we first investigate the fundamental limits of FAS-RIS systems with RZF/ZF precoding by deriving the ergodic sum rate (ESR). This result is utilized to determine the minimum number of selected antennas to achieve a given ESR. Based on the evaluation result, we propose an algorithm to jointly optimize the antenna selection, regularization factor of RZF, and phase shifts at the RIS. Numerical results validate the accuracy of performance evaluation and demonstrate that the performance gain brought by joint FAS and RIS design is more pronounced with a larger number of users.
Theta Theory: operads and coloring
Venue: arXiv cs.CL 2025
Year: 2025

We give an explicit construction of the generating set of a colored operad that implements theta theory in the mathematical model of Minimalism in generative linguistics, in the form of a coloring algorithm for syntactic objects. We show that the coproduct operation on workspaces allows for a recursive implementation of the theta criterion. We also show that this filtering by coloring rules on structures freely formed by Merge is equivalent to a process of structure formation by a colored version of Merge: the form of the generators of the colored operad then implies the dichotomy is semantics between External and Internal Merge, where Internal Merge only moves to non-theta positions.
Handwritten Digit Recognition: An Ensemble-Based Approach for Superior Performance
Venue: arXiv cs.CV 2025
Year: 2025

Handwritten digit recognition remains a fundamental challenge in computer vision, with applications ranging from postal code reading to document digitization. This paper presents an ensemble-based approach that combines Convolutional Neural Networks (CNNs) with traditional machine learning techniques to improve recognition accuracy and robustness. We evaluate our method on the MNIST dataset, comprising 70,000 handwritten digit images. Our hybrid model, which uses CNNs for feature extraction and Support Vector Machines (SVMs) for classification, achieves an accuracy of 99.30%. We also explore the effectiveness of data augmentation and various ensemble techniques in enhancing model performance. Our results demonstrate that this approach not only achieves high accuracy but also shows improved generalization across diverse handwriting styles. The findings contribute to the development of more reliable handwritten digit recognition systems and highlight the potential of combining deep learning with traditional machine learning methods in pattern recognition tasks.
GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Using Large Language Models (LLMs) to evaluate and compare two answers from different models typically involves having LLM-based judges select the better answer. However, humans often approach problem-solving from a reverse perspective, for instance, by choosing the worse option instead of the better one in a pairwise comparison. Generally, this kind of reverse thinking plays a crucial role in human reasoning and decision-making and can further test the difference between original and reverse thought processes simultaneously. To address the above issue, in this paper, we propose a Goal-Reversed Prompting (GRP) approach for pairwise evaluation that shifts the original task from selecting the better answer to choosing the worse one. We encourage LLMs to think in reverse by prompting LLMs to identify the worse response. Experiments on closed-source models demonstrate that GRP significantly enhances evaluation capabilities, outperforming the prompt template with the original goal.
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Minimally invasive surgery (MIS) requires high-fidelity, real-time visual feedback of dynamic and low-texture surgical scenes. To address these requirements, we introduce FeatureEndo-4DGS (FE-4DGS), the first real time pipeline leveraging feature-distilled 4D Gaussian Splatting for simultaneous reconstruction and semantic segmentation of deformable surgical environments. Unlike prior feature-distilled methods restricted to static scenes, and existing 4D approaches that lack semantic integration, FE-4DGS seamlessly leverages pre-trained 2D semantic embeddings to produce a unified 4D representation-where semantics also deform with tissue motion. This unified approach enables the generation of real-time RGB and semantic outputs through a single, parallelized rasterization process. Despite the additional complexity from feature distillation, FE-4DGS sustains real-time rendering (61 FPS) with a compact footprint, achieves state-of-the-art rendering fidelity on EndoNeRF (39.1 PSNR) and SCARED (27.3 PSNR), and delivers competitive EndoVis18 segmentation, matching or exceeding strong 2D baselines for binary segmentation tasks (0.93 DSC) and remaining competitive for multi-label segmentation (0.77 DSC).
VACT: A Video Automatic Causal Testing System and a Benchmark
Venue: arXiv cs.AI 2025
Year: 2025

With the rapid advancement of text-conditioned Video Generation Models (VGMs), the quality of generated videos has significantly improved, bringing these models closer to functioning as ``*world simulators*'' and making real-world-level video generation more accessible and cost-effective. However, the generated videos often contain factual inaccuracies and lack understanding of fundamental physical laws. While some previous studies have highlighted this issue in limited domains through manual analysis, a comprehensive solution has not yet been established, primarily due to the absence of a generalized, automated approach for modeling and assessing the causal reasoning of these models across diverse scenarios. To address this gap, we propose VACT: an **automated** framework for modeling, evaluating, and measuring the causal understanding of VGMs in real-world scenarios. By combining causal analysis techniques with a carefully designed large language model assistant, our system can assess the causal behavior of models in various contexts without human annotation, which offers strong generalization and scalability. Additionally, we introduce multi-level causal evaluation metrics to provide a detailed analysis of the causal performance of VGMs. As a demonstration, we use our framework to benchmark several prevailing VGMs, offering insight into their causal reasoning capabilities. Our work lays the foundation for systematically addressing the causal understanding deficiencies in VGMs and contributes to advancing their reliability and real-world applicability.
Integration of SDN and Digital Twin for the Intelligent Detection of DoC Attacks in WRSNs
Venue: arXiv cs.NI 2025
Year: 2025

Wireless rechargeable sensor networks (WRSNs), supported by recent advancements in wireless power transfer (WPT) technology, hold significant potential for extending network lifetime. However, traditional approaches often prioritize scheduling algorithms and network optimization, overlooking the security risks associated with the charging process, which exposes the network to potential attacks. This paper addresses this gap by integrating Software-Defined Networking (SDN) and Digital Twin technologies for the intelligent detection of Denial of Charging (DoC) attacks in WRSNs. First, it leverages the flexibility and intelligent control of SDN, in combination with Digital Twin, to enhance real-time detection and mitigation of DoC attacks. Second, it employs four key metrics to detect such attacks including charging request patterns, energy consumption, behavioral and reputation scores, and charging behavior and efficiency. The numerical results demonstrate the superior performance of the proposed protocol in terms of energy usage efficiency, survival rate, detection rate, and travel distance.
Object-Centric World Model for Language-Guided Manipulation
Venue: arXiv cs.AI 2025
Year: 2025

A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained significant attention due to the impressive success of diffusion models. However, these models require substantial computational resources. To address these challenges, we propose a world model leveraging object-centric representation space using slot attention, guided by language instructions. Our model perceives the current state as an object-centric representation and predicts future states in this representation space conditioned on natural language instructions. This approach results in a more compact and computationally efficient model compared to diffusion-based generative alternatives. Furthermore, it flexibly predicts future states based on language instructions, and offers a significant advantage in manipulation tasks where object recognition is crucial. In this paper, we demonstrate that our latent predictive world model surpasses generative world models in visuo-linguo-motor control tasks, achieving superior sample and computation efficiency. We also investigate the generalization performance of the proposed method and explore various strategies for predicting actions using object-centric representations.
ROCM: RLHF on consistency models
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models have revolutionized generative modeling in continuous domains like image, audio, and video synthesis. However, their iterative sampling process leads to slow generation and inefficient training, challenges that are further exacerbated when incorporating Reinforcement Learning from Human Feedback (RLHF) due to sparse rewards and long time horizons. Consistency models address these issues by enabling single-step or efficient multi-step generation, significantly reducing computational costs. In this work, we propose a direct reward optimization framework for applying RLHF to consistency models, incorporating distributional regularization to enhance training stability and prevent reward hacking. We investigate various $f$-divergences as regularization strategies, striking a balance between reward maximization and model consistency. Unlike policy gradient methods, our approach leverages first-order gradients, making it more efficient and less sensitive to hyperparameter tuning. Empirical results show that our method achieves competitive or superior performance compared to policy gradient based RLHF methods, across various automatic metrics and human evaluation. Additionally, our analysis demonstrates the impact of different regularization techniques in improving model generalization and preventing overfitting.
Minion Gated Recurrent Unit for Continual Learning
Venue: arXiv cs.LG 2025
Year: 2025

The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Consequently, this has widened the knowledge gap between continual learning with recurrent neural networks (RNNs) and their ability to operate on devices with limited memory and compute. To address this challenge, we investigate the effectiveness of simplifying RNN architectures, particularly gated recurrent unit (GRU), and its impact on both single-task and multitask sequential learning. We propose a new variant of GRU, namely the minion recurrent unit (MiRU). MiRU replaces conventional gating mechanisms with scaling coefficients to regulate dynamic updates of hidden states and historical context, reducing computational costs and memory requirements. Despite its simplified architecture, MiRU maintains performance comparable to the standard GRU while achieving 2.90x faster training and reducing parameter usage by 2.88x, as demonstrated through evaluations on sequential image classification and natural language processing benchmarks. The impact of model simplification on its learning capacity is also investigated by performing continual learning tasks with a rehearsal-based strategy and global inhibition. We find that MiRU demonstrates stable performance in multitask learning even when using only rehearsal, unlike the standard GRU and its variants. These features position MiRU as a promising candidate for edge-device applications.
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused. In this study, we empirically show that different timesteps of DDIM inversion reveal varying subtle distinctions between synthetic and real images that are extractable for detection, in the forms of such as Fourier power spectrum high-frequency discrepancies and inter-pixel variance distributions. Based on these observations, we propose a novel synthetic image detection method that directly utilizes features of intermediately noised images by training an ensemble on multiple noised timesteps, circumventing conventional reconstruction-based strategies. To enhance human comprehension, we introduce a metric-grounded explanation generation and refinement module to identify and explain AI-generated flaws. Additionally, we construct the GenHard and GenExplain benchmarks to provide detection samples of greater difficulty and high-quality rationales for fake images. Extensive experiments show that our method achieves state-of-the-art performance with 98.91% and 95.89% detection accuracy on regular and challenging samples respectively, and demonstrates generalizability and robustness. Our code and datasets are available at https://github.com/Shadowlized/ESIDE.
Late Fusion and Multi-Level Fission Amplify Cross-Modal Transfer in Text-Speech LMs
Venue: arXiv cs.CL 2025
Year: 2025

Text-Speech Language Models (TSLMs) -- language models trained to jointly process and generate text and speech -- are commonly trained through an early modality fusion/fission approach, in which both modalities are fed and predicted from a shared backbone via linear layers. We hypothesize that this approach limits cross-modal transfer by neglecting feature compositionality -- specifically, the finer-grained nature of speech representations compared to text -- preventing the emergence of a shared feature hierarchy within model layers. In this paper, we argue that this limitation can be addressed through late fusion and fission, with a fission process that accesses both high- and low-level features for speech generation. Our models implementing these principles, SmolTolk, rival or surpass state-of-the-art TSLMs trained with orders of magnitude more compute, and achieve significantly improved cross-modal performance relative to early fusion/fission baselines. Representation analyses further suggest that our method enhances the model's ability to abstract higher-level, more semantic features from speech, and leads to increasingly shared representation spaces across layers.
GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs
Venue: arXiv cs.LG 2025
Year: 2025

Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges. To enable efficient training on such large graphs, mini-batch subgraph sampling is commonly used, which allows training without loading the entire graph into memory. However, existing solutions face significant trade-offs: online subgraph generation, as seen in frameworks like DGL and PyG, is limited to a single machine, resulting in severe performance bottlenecks, while offline precomputed subgraphs, as in GraphGen, improve sampling efficiency but introduce large storage overhead and high I/O costs during training. To address these challenges, we propose \textbf{GraphGen+}, an integrated framework that synchronizes distributed subgraph generation with in-memory graph learning, eliminating the need for external storage while significantly improving efficiency. GraphGen+ achieves a \textbf{27$\times$} speedup in subgraph generation compared to conventional SQL-like methods and a \textbf{1.3$\times$} speedup over GraphGen, supporting training on 1 million nodes per iteration and removing the overhead associated with precomputed subgraphs, making it a scalable and practical solution for industry-scale graph learning.
A Frank System for Co-Evolutionary Hybrid Decision-Making
Venue: arXiv cs.HC 2025
Year: 2025

We introduce Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making aiding the user to label records from an un-labeled dataset. Frank employs incremental learning to ``evolve'' in parallel with the user's decisions, by training an interpretable machine learning model on the records labeled by the user. Furthermore, Frank advances state-of-the-art approaches by offering inconsistency controls, explanations, fairness checks, and bad-faith safeguards simultaneously. We evaluate our proposal by simulating the users' behavior with various levels of expertise and reliance on Frank's suggestions. The experiments show that Frank's intervention leads to improvements in the accuracy and the fairness of the decisions.
Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Traditional white-box methods for creating adversarial perturbations against LLMs typically rely only on gradient computation from the targeted model, ignoring the internal mechanisms responsible for attack success or failure. Conversely, interpretability studies that analyze these internal mechanisms lack practical applications beyond runtime interventions. We bridge this gap by introducing a novel white-box approach that leverages mechanistic interpretability techniques to craft practical adversarial inputs. Specifically, we first identify acceptance subspaces - sets of feature vectors that do not trigger the model's refusal mechanisms - then use gradient-based optimization to reroute embeddings from refusal subspaces to acceptance subspaces, effectively achieving jailbreaks. This targeted approach significantly reduces computation cost, achieving attack success rates of 80-95\% on state-of-the-art models including Gemma2, Llama3.2, and Qwen2.5 within minutes or even seconds, compared to existing techniques that often fail or require hours of computation. We believe this approach opens a new direction for both attack research and defense development. Furthermore, it showcases a practical application of mechanistic interpretability where other methods are less efficient, which highlights its utility. The code and generated datasets are available at https://github.com/Sckathach/subspace-rerouting.
MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering
Venue: arXiv cs.CL 2025
Year: 2025

Question Answering (QA) and Visual Question Answering (VQA) are well-studied problems in the language and vision domain. One challenging scenario involves multiple sources of information, each of a different modality, where the answer to the question may exist in one or more sources. This scenario contains richer information but is highly complex to handle. In this work, we formulate a novel question-answer generation (QAG) framework in an environment containing multi-source, multimodal information. The answer may belong to any or all sources; therefore, selecting the most prominent answer source or an optimal combination of all sources for a given question is challenging. To address this issue, we propose a question-guided attention mechanism that learns attention across multiple sources and decodes this information for robust and unbiased answer generation. To learn attention within each source, we introduce an explicit alignment between questions and various information sources, which facilitates identifying the most pertinent parts of the source information relative to the question. Scalability in handling diverse questions poses a challenge. We address this by extending our model to a sparse mixture-of-experts (sparse-MoE) framework, enabling it to handle thousands of question types. Experiments on T5 and Flan-T5 using three datasets demonstrate the model's efficacy, supported by ablation studies.
On the Fly Adaptation of Behavior Tree-Based Policies through Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

With the rising demand for flexible manufacturing, robots are increasingly expected to operate in dynamic environments where local -- such as slight offsets or size differences in workpieces -- are common. We propose to address the problem of adapting robot behaviors to these task variations with a sample-efficient hierarchical reinforcement learning approach adapting Behavior Tree (BT)-based policies. We maintain the core BT properties as an interpretable, modular framework for structuring reactive behaviors, but extend their use beyond static tasks by inherently accommodating local task variations. To show the efficiency and effectiveness of our approach, we conduct experiments both in simulation and on a Franka Emika Panda 7-DoF, with the manipulator adapting to different obstacle avoidance and pivoting tasks.
Higher-Order Belief in Incomplete Information MAIDs
Venue: arXiv cs.AI 2025
Year: 2025

Multi-agent influence diagrams (MAIDs) are probabilistic graphical models which represent strategic interactions between agents. MAIDs are equivalent to extensive form games (EFGs) but have a more compact and informative structure. However, MAIDs cannot, in general, represent settings of incomplete information -- wherein agents have different beliefs about the game being played, and different beliefs about each-other's beliefs. In this paper, we introduce incomplete information MAIDs (II-MAIDs). We define both infinite and finite-depth II-MAIDs and prove an equivalence relation to EFGs with incomplete information and no common prior over types. We prove that II-MAIDs inherit classical equilibria concepts via this equivalence, but note that these solution concepts are often unrealistic in the setting with no common prior because they violate common knowledge of rationality. We define a more realistic solution concept based on recursive best-response. Throughout, we describe an example with a hypothetical AI agent undergoing evaluation to illustrate the applicability of II-MAIDs.